Forecasting Tourist Arrivals Using a Combination of Long Short-Term Memory and Fourier Series

Shabri, Ani and Samsudin, Ruhaidah and Saeed, Faisal and Al-Sarem, Mohammed (2023) Forecasting Tourist Arrivals Using a Combination of Long Short-Term Memory and Fourier Series. In: ICACIn 2022: Advances on Intelligent Computing and Data Science, 24th - 25th November 2022, Casablanca, Morocco.

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The sector that contributes most to the nation's economy nowadays is tourism. Policymakers, decision-makers, and organisations involved in the tourist sector can use tourism demand forecasting to gather important information for planning and making important decisions. However, it is difficult to produce an accurate forecast because tourism data is critical, especially when a periodic pattern, such as seasonality, trends, and non-linearity, is present in a dataset. In this research, we present a hybrid modelling approach for modelling tourist arrivals time series data that combines the long short-term memory (LSTM) with the Fourier series method. This method is proposed to capture the components of seasonality and trend in the data set. Various single models, such as ARIMA and LSTM, as well as a modified ARIMA model based on Fourier series, are evaluated to confirm the suggested model's accuracy. The efficiency of the proposed model is compared using monthly tourism arrivals data from Langkawi Island, which has a notable pattern and seasonality. The findings reveal that the proposed model is more reliable than the other models in forecasting tourist arrivals series.

Item Type: Conference or Workshop Item (Paper)
1 August 2023Accepted
17 August 2023Published Online
Uncontrolled Keywords: Fourier series, artificial neural network, long short-term memory, ARIMA, tourist arrival.
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Gemma Tonks
Date Deposited: 15 Feb 2024 16:10
Last Modified: 19 Mar 2024 15:49

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